Cheat Sheet
CLI Commands
Documentation tools
search_rasa_documentation
Search the official Rasa documentation for authoritative information. Returns relevant documentation about Rasa concepts, APIs, best practices, configuration, and troubleshooting with links to official docs.
Returns: Matching documentation snippets with source links.
Sample prompts:
Project introspection tools
list_project_flow_definitions
List all flow definitions in the project. Returns flow ID, name, and file path for each flow.
Returns: List of flows with
id, name, and file_path.
Sample prompt:
list_project_slot_definitions
List all slot definitions from the project domain file(s). Returns slot name, type, and file path.
Returns: List of slots with
name, type, and file_path.
Sample prompt:
list_project_response_definitions
List all response (utterance) definitions from the project domain file(s). Returns response name and file path.
Returns: List of responses with
name and file_path.
Sample prompt:
get_flow
Get a single flow by flow ID (YAML key) or flow name. Returns flow metadata and full definition including steps, triggers, and branching logic.
Returns: Full flow definition with metadata.
Sample prompt:
get_slot
Get a single slot by name. Returns slot metadata and full definition from the domain.
Returns: Full slot definition with type, mappings, and metadata.
Sample prompt:
get_response
Get a single response (utterance) by name. Returns response metadata and full definition including text, images, buttons, and custom payloads.
Returns: Full response definition with all variations.
Sample prompt:
list_project_custom_actions_in_domain
List all custom actions declared in the domain file(s). Returns action name and file path where the action is registered.
Returns: List of custom action names with
name and file_path. This lists domain declarations, not Python implementations.
Sample prompt:
list_custom_action_implementations
List all custom action Python implementations in the project. Returns action name, class name, and file path for each action.
Returns: List of action implementations with
action_name, class_name, and file_path.
Notes:
- If
erroris set: the actions folder was not found. Checkendpoints.ymlor specify the folder. - If
count=0and no error: the actions folder exists but contains no action classes.
list_default_action_names
List all built-in default action names provided by Rasa. These actions are available without configuration and can be overridden.
Parameters: None.
Returns: List of default action name strings.
Sample prompt:
Schema tools
get_flow_schema
Get the official Rasa flow schema in JSON Schema format. Use this to validate flow YAML or generate new flows.
Returns: JSON Schema document describing flow structure: name, description, step types, branching logic (
if/then/else), collect steps, flow guards, and more.
Sample prompt:
get_domain_schema
Get the official Rasa domain schema in YAML schema format. Use this to validate domain YAML or generate domain files.
Returns: YAML schema document describing domain structure: slots, custom actions, responses, and more.
Sample prompt:
get_e2e_schema
Get the official Rasa E2E test schema in YAML schema format. Use this to validate or generate end-to-end test files.
Parameters: None.
Returns: YAML schema document describing E2E test structure: test cases, steps (user and bot messages), fixtures, metadata, stub custom actions, and assertions.
Sample prompt:
Build and validation tools
validate_project
Validate the assistant project configuration and training data. Runs comprehensive checks on domain, flows, config, and training data.
Parameters: None (reads from the configured project folder).
Returns: Pass/fail status with a list of errors and warnings.
Notes:
- Can take 60+ seconds for large projects.
- Always run this after making changes and before training.
train_rasa_assistant
Train the Rasa assistant with the current project configuration. Creates a new model in the models/ directory.
Parameters: None (reads from the configured project folder).
Returns: Training status (success/failure), model path, and any errors.
Notes:
- Can take several minutes for large projects.
- Only call this after validation passes.
- Each training run produces a new timestamped model file.
Runtime testing and debugging tools
talk_to_assistant
Test the assistant by sending a sequence of messages and verifying responses. Creates a new conversation for each call.
Returns: Structured response with:
- Conversation history (user messages and assistant responses)
- Tracker context showing conversation state, active flows, and slot values
rasa run or rasa run --inspect).
Sample prompts:
get_assistant_logs
Get recent log entries from the Rasa assistant for troubleshooting.
Parameters: None.
Returns: Recent log entries as text.
Sample prompts:
Simulation and evaluation tools
v3.17validate_scenario
Validate a scenario YAML file before running any simulation. Checks syntax and structure, assertion type validity, and that all slot names referenced in initial_slots, slot_was_set, and slot_was_not_set assertions exist in the domain with compatible types. All errors are reported together in a single response.
Returns: Pass/fail status with a list of all validation errors.
Sample prompts:
evaluate_agent
Run a simulation and evaluation loop for a given scenario. Loads eval/conftest.yml, simulates a multi-turn conversation with an LLM-based user against the running Rasa server, evaluates deterministic assertions against the tracker event history, and scores quality criteria and metrics with an LLM judge. Writes per-run result files and updates the experiment summary.
Returns: Pass/fail verdict, runs passed/total count, and the path to the updated
summary.txt.
Prerequisites: The Rasa assistant must be running (rasa run --inspect recommended so that simulated conversations are viewable in the Inspector).
Notes:
- A run is marked failed if any deterministic assertion fails or any quality criterion fails.
- Quality metrics (
task_completionand aggregatedbot_quality—helpfulness,repair_quality,coherence,tone) are informational and do not gate the pass/fail verdict. - On timeout, completed
run_N.txtfiles are preserved butsummary.txtis not updated.
Typical workflow
A common sequence for building a feature end-to-end:- Discover —
list_project_flow_definitions,list_project_slot_definitions,list_project_response_definitions - Understand schemas —
get_flow_schema,get_domain_schema,get_e2e_schema - Implement — write flows, domain entries, and custom actions
- Validate —
validate_project - Train —
train_rasa_assistant - Test —
talk_to_assistant - Debug —
get_assistant_logsif behavior is unexpected - Evaluate —
validate_scenario,evaluate_agentto run LLM-simulated conversations and score them against your goals
Related
- Rasa MCP Tools — setup and client configuration
- Simulation and Evaluation — scenario YAML schema, assertion types, conftest configuration, and result file formats
- Command Line Interface — Rasa CLI reference
- Prompt-Driven Agent Tutorial — build features with prompts